Achieve Excellence through Customer Experience

Achieve Excellence through
Customer Experience
Naveen Agarwal, Ph.D.
Email: Info@ExeedQM.com
Web: www.ExeedQM.com
OPEX 2018
Manufacturing Excellence
January 25th, 2018
© Creative Analytics Solutions, LLCInnovative Quality Solutions

© Creative Analytics Solutions, LLC
25th Jan 2018 Excellence Through Customer Experience Slide 2
Outline
1 Quick Introduction – My Story and J&J Vision Care Overview
2 Customer Experience Defined and Why it Matters
3 Case Study – Strategic Quality Improvement Project
4 Systematic Process for Post-market Surveillance
5 Case Study – Signal Detection, Assessment and Action
6 Leveraging Big Data
7 Sustaining a Customer Focused Culture

M.S.. Engineering
Product Development
New Ventures
Business Analytics
Product Quality
Technology Development
My Story……….. Ph.D. Engineering
Product Development
DFSS Black Belt
Innovative Quality
Solutions
1

J&J Vision Care, Inc. – Manufacturer of Acuvue®
The World’s Best Selling Contact Lens Brand
 $2.8 billion global sales in 2016
 2 Manufacturing locations – Jacksonville, FL
and Limerick, Ireland
 Highly automated, high speed manufacturing
 Global distribution – products sold in over
100 countries
 Recently acquired Abbott Medical Optics
(AMO) and Tear Sciences
Sources: J&J Annual Report, Acuvue.com
1

2

Customers React to Variation in Experience
2

Each Customer Experience Matters
2

Customer Expectations Shift Over Time
“Good enough” yesterday is no longer “good” today
2

Regulated Industries - Business Case for Change
Customer
Expectation /
Patient Safety
Consistency
Payors
conducting
Healthcare
Assessments
Regulatory Shifts
Increasing
Complexity
Increased Data
Availability
Standardization
Across Enterprise
Design To Value
External
Advanced Data
Mining
Capabilities
Shifts in market and regulatory environment is driving change at the Enterprise level
2

FDA Focus on Data and Surveillance
 223 MM members (2000-2016)
 17 partner institutions
 425 million person-years observation time
 5.9 billion pharmacy dispensing
 7.2 billion unique medical encounters
 42 million people with at least one lab test
result
Source: Sentinelinitiative.org, fda.gov/safety/FDASentinalinitiative
Distributed Database and Common Data Model
The active surveillance capability of the Sentinel System does not replace the FDA’s existing surveillance tools, but complements other FDA
safety surveillance capabilities by allowing the FDA to proactively assess the safety of regulated medical products.
2

Let us look at a case study …….
3

Case Study – Understanding Customer Experience
Customers Complaining about
1. Misshaped or folded lenses
2. Vision issues
3. General Discomfort
4. Lenses with edge defects
Internal Perspective
1 Complaint
Lot size = 100,000 lenses
DPMO = 10
Sigma Level = 5.7
1 Complaint
Annual Supply = 365 lenses
DPMO = 2739
Sigma Level = 4.3
Customer Perspective
3

Case Study - Strategic Quality Improvement Project
US Patent 9,723,903 B2
ROOT CAUSE SOLUTIONCROSS-FUNCTIONAL
3

A Systematic Approach
4

Risk Based Approach
A risk based approach applicable to a broad range of device categories is needed to have consistency across the organization
Contact Lenses Hip Joints Cardiac Ablation Catheters
Low Risk
Low Severity
Medium Risk
Medium Severity
High Risk
High Severity
4

Safety Assessment Matrix
Product Risk Profile
• Severity of adverse events
• History of adverse events
• Detectability of device malfunction
• User interface (simple or complex to use)
• Severity of outcome as a result of error in use
• User contact or exposure time to device
Quality of Data
• Timeliness
• Accuracy
• Distance from end user
• Lack of literature
Very Low Low Moderate High Very High
Good
Fair
Poor
Enhanced PSS optional
Enhanced PSS should be
considered
Enhanced PSS
recommended
Tailor the scale to your business and products. Develop questions and criteria to cover all relevant
factors of product risk profile and data quality
4

• Perform Process Audits and
Identify Non-conformances
• Conduct Stakeholder
Reviews (at least annually)
to Measure Effectiveness
• Identify Improvements in
Process and Signal
Assessment Methods
• Corrective and Preventive
Actions on Escalated Signals
• Improve Applicable Design
and Development Plans
• Improve Process and
Methods
Framework of a Post-market Surveillance System
• Establish PSS Team
• Establish Standard
Operating Procedures
(SOP)
• Establish Product Safety
Surveillance Plans (SSP)
• Establish Signal Assessment
Methods
• Develop Escalation Plans
• Identify Signals Using
Advanced Analytics
• Apply Signal Escalation
Criteria to Confirm Signals
• Document Action Plan (or
Justification for No Action)
4

Key Components of a PSS System
Executive
Level
Governance
Analytics
Team
SSPs
SOPs &
Records
Cross Functional Team
Product Specialists
Manufacturing
Medical Safety
Customer Relations
Quality Professionals
Safety Surveillance Plans
Product or Product Category
Specific
Define Data Sources,
Type/Frequency of Analysis
Risk Based Approach
Analytics
Traditional Statistics
• Time Series
• PRR
Machine Learning
Procedures and Records
Define Scope
Define Process & Methods
Define Roles and
Responsibilities
Compatible with Existing
Quality System
4

Product Lifecycle Risk Management
4

Simple Governance Structure
Executive Level
Advisory Panel
Top Level Oversight
 Quality and safety collaboration
 Establish requirements
 Assign resources
 Measure effectiveness
Core Team
Core Operations Team
 Cross functional
 Develop procedures
 Execute procedures
 Escalate issues
R&D Mfg. Mktg. Reg.
Clinic
Trials
Ad-hoc Extended Cross-Functional Support
4

Another Case Study….
Detecting Quality Issues
5

Case Study – Detecting Quality Signals
5

Effect of
CAPA
Trigger
Time Series Analysis for Action and Follow Up
Confirm
5

Detecting Low Frequency Issues
Proportional Reporting Ratio (PRR) assesses frequency of a
product-specific event relative to other similar products
Event (R) All Other
Events
Total
Product (P) A B A+B
Other Similar
Products
C D C+D
Total A+C B+D N=A+B+C+D
Standard Deviation =>
95% Confidence Interval =>
As an example, we can trigger a signal if the lower
bound on PRR exceeds 1
Say, we are tracking the frequency of serious medical events
related to a device with respect to all other medical events
and we find the following data in a given month
MDR All Other
Medical
Total
Product (P) 2 46 48
Other Similar
Products
6 822 828
Total 8 868 876
According to our rule, we will trigger this signal as a
“potential” signal – Should we act?
PRR = 5.75
Lower Bound = 1.54
5

Other Methods – Text Analytics
Data-mining of verbal feedback from customers about their actual experience, or their conversations on social media can provide
insights into patterns and possibly early warning of an issue
As an example, experience of general discomfort with soft contact lens wear is very hard to quantify and understand. By monitoring
frequency of “key words” associated with this experience, we can better understand shifts in customer experience over time
We can study:
1. Time series of indicators
2. Correlations between indicators
3. Correlation to demographic or
geographic factors
4. Association with specific product lots
or manufacturing timeframe to
indicate potential impact of variation
5

6
Leveraging Big Data….

Understanding Customer Experience
The Big Data Problem?
World as we know it! Big Data World!
6

Big Data = Volume, Variety and Velocity
Structured Data Unstructured Data
Employee Data
Sales Data
Survey Data
Lifestyle
Data
Geo Data
Vision Test
Data
Complaints
Data
Search
Data
Social Media
Chatter
Video Data
Voice Data
Image Data
Calls Data

Case of Complaints – What’s Next?
Cluster Analysis
Anomaly Detection
Classification
Sentiment Analysis
Data Mining
NLP

Traditional Statistical Analytics Tools
25th Jan 2018 Slide 30
Descriptive
What happened?
Inferential
Why?
Predictive
What could
happen?
Prescriptive
What should
we do?
Basic reporting
• Summarize past data
• Mean, median, mode,
min, max, variance
• Growth rates
• Compare against
benchmarks or goals
• Data distributions,
process capability
• Charts, graphs, tables to
visualize simple trends
Basic prediction
• Relating sample data to
general population
• Finding statistically
significant factors
• Regression Analysis
• Correlations
• Hypothesis testing
• DOE, ANOVA, GLM
• Multivariate analysis
Forecasting
• Delphi methods
• Trend analysis
extrapolation
• Moving averages, data
smoothing
• Time series, ARIMA
• Regression analysis
Future outcomes
• Data modeling and
simulations
• Sensitivity analysis
• What-ifs and
probabilities
• Decision tree analysis
• DOE/Robust Design and
optimization
6

Next Frontier - AI > ML > DL
Artificial Intelligence (AI) – enables machines to have human-like intelligence
Machine Learning (ML) – enables machines to “learn” with experience
Deep Learning (DL) – enables machines to build a “brain”
6

Common ML Algorithms
Logistic
Regression
Decision
Tree
Random
Forest
Neural
Networks
Polynomial
Regression
Linear
Regression
Linear equation
best fit
Adapted for yes/no
Uses sigmoid function
Polynomial equation
best fit
Mapping all outcomes
and probabilities
Multiple decision trees
Random sampling
Interconnected layers of
nodes like a human brain
 Simple calculations
 Easy to train/validate
 Numerical prediction
 Main factors
x No higher order terms
x Model optimization issues
x Feature scaling
x Overfitting issues
 Easy to visualize
relationships
 Easy to train/validate
x Over simplification
x Limited predictability
 “Average” of many
 Fast to train
 Higher quality
output
x Slow to output
predictions
x Difficult to interpret
 Model complex tasks
 High accuracy
 Suitable for deep
learning
x Very slow to train
x Challenging to
optimize
x Nearly impossible
to interpret
6

Sustaining Change…
Changing Culture
7

Sustaining a Customer Focused Culture
Values
Management
Structure
Policy
Procedures
Organizational
Design
Rewards
Recognition
7

About ExeedTM
Portfolio of Innovative Quality Solutions in 4 Broad Areas
Email: Info@ExeedQM.com
Web: www.ExeedQM.com
Phone: 1-833-MY-EXEED
Customer Experience Regulatory Compliance
Risk Management Quality Culture

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